Nepal Hazard Risk Assessment - Asia-Pacific Gateway for Disaster ...

Nepal Hazard Risk Assessment
No Station name District Winter Premonsoon Monsoon Postmonsoon
0802 KHUDI BAZAR Lamjung 41 265 2405 29
0804 POKHARA AIRPORT Kaski 39 405 2612 36
0809 GORKHA Gorkha 25 182 914 5
0814 LUMLE Kaski 59 378 3984 126
0815 KHAIRINI TAR Tanahun 28 305 1424 20
0902 RAMPUR Chitawan 9 139 1362 26
0905 DAMAN Makwanpur 12 171 966 9
0906 HETAUNDA N.F.I. Makwanpur 12 170 1625 36
0909 SIMARA AIRPORT Bara 10 101 1172 20
0911 PARWANIPUR Bara 11 88 918 22
1007 KAKANI Nuwakot 25 206 1892 37
1029 Khumal tar Lalitpur 15 118 745 12
1043 NAGARKOT Kathmandu 12 150 1177 20
1103 JIRI Dolkha 18 237 1418 34
1111 JANAKPUR AIRPORT Dhanusa 9 60 837 3
1206 OKHALDHUNGA Okhaldhunga 20 147 1184 21
1303 CHAINPUR (EAST) Sankhuwasabh 16 240 815 23
a
1307 DHANKUTA Dhankuta 13 131 580 9
1319 BIRATNAGAR AIRPOART Morang 7 151 1090 13
1320 TARAHARA Sunsari 9 190 1157 28
1405 TAPLEJUNG Taplejung 36 471 1367 38
1407 ILAM TEA ESTATE Ilam 10 142 801 15
1416 KANYAM TEA ESTATE Ilam 22 285 1620 33
(d) Monitoring Drought
Monthly SPI provides a lot of useful information about the drought. Time series plot of SPI (figure 5: a
and b) shows the onset of drought, and its duration. Further, it provided information on drought intensity.
Therefore SPI is a very good index for monitoring the drought. For example Figure 3.5 is the time series
plot of monthly SPI for Jumla from 2003 to 2004. Figure 3.6 shows that the 2005-2006 winter drought
started from December, reached its highest intensity during February, lasted for almost three months and
ended in March.
(e) Limitation
Besides its advantages, practical applications of the SPI possess some disadvantages (Guttman, 1999). It
is assumed that a suitable theoretical probability distribution can be found to model the raw precipitation
data prior to standardization (Hayes et al., 1999). Another limitation of the SPI emerges from the
standardization process of the index itself. Drought measured by the SPI can occur with same frequency
at all locations when considered over a long time period. A third problem is that misleadingly large
positive or negative SPI values may result when the index is applied at short time steps to regions of low
seasonal precipitation (Sönmez et al, 2005). In analysis of short duration (2 months) seasonal data, SPI
may not show drought condition even when there is high rainfall deficit or no rainfall at all during some
years.
SPI
1.5
1
0.5
0
-0.5
-1
-1.5
-2
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2003 2004
Year/Month
SPI
2
1.5
1
0.5
0
-0.5
-1
-1.5
-2
-2.5
-3
3.2 THE SOIL MOISTURE INDEX (SMI)
Figure 3.5: Time series plot of monthly SPI for Simara from 2003 to 2004
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2005 2006
Year/Month
Figure 3.6. Time series plot of monthly SPI for Simara from 2005 to 2006
It was noted above that Monthly SPI is useful method for drought monitoring in the scale of a month to
few years but SPI can occur with same frequency at all locations when considered over a long time
period so is not effective in monitoring long term variation in soil moisture. On the other hand, SMI is
useful for monitoring the (seasonal) soil moisture variation over long periods (a decade or more) which
could be useful specially in the context of climate change.
110